Single-channel mixed signal blind source separation algorithm based on multiple ICA processing

2017 ◽  
Author(s):  
Xiefeng Cheng ◽  
Ji Li
Keyword(s):  
Blind Source Separation ◽  
Single Channel ◽  
Source Separation ◽  
Separation Algorithm ◽  
Mixed Signal

scholarly journals Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4844
Author(s):  
Mengchen Zhao ◽  
Xiujuan Yao ◽  
Jing Wang ◽  
Yi Yan ◽  
Xiang Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Blind Source Separation ◽  
Single Channel ◽  
Source Separation ◽  
Feature Representation ◽  
Noise Robustness ◽  
Separation Performance ◽  
Mixed Signal ◽  
Source Signal ◽  
Channel Separation

Aiming at the problem of insufficient separation accuracy of aliased signals in space Internet satellite-ground communication scenarios, a stacked long short-term memory network (Stacked-LSTM) separation method based on deep learning is proposed. First, the coding feature representation of the mixed signal is extracted. Then, the long sequence input is divided into smaller blocks through the Stacked-LSTM network with the attention mechanism of the SE module, and the deep feature mask of the source signal is trained to obtain the Hadamard product of the mask of each source and the coding feature of the mixed signal, which is the encoding feature representation of the source signal. Finally, characteristics of the source signal is decoded by 1-D convolution to to obtain the original waveform. The negative scale-invariant source-to-noise ratio (SISNR) is used as the loss function of network training, that is, the evaluation index of single-channel blind source separation performance. The results show that in the single-channel separation of spatially aliased signals, the Stacked-LSTM method improves SISNR by 10.09∼38.17 dB compared with the two classic separation algorithms of ICA and NMF and the three deep learning separation methods of TasNet, Conv-TasNet and Wave-U-Net. The Stacked-LSTM method has better separation accuracy and noise robustness.

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